Lee H, et al. J Korean Med Sci. 2017 Oct;32(10):1674-1679. https://doi.org/10.3346/jkms.2017.32.10.1674

Purpose: The excessive use of smartphones for social interaction is associated with mental health. However, few studies have considered the purpose of smartphone usage and screen time together. Therefore, we investigated the impact of smartphone purpose and screen time on mental health using data from the 2017 Korea Youth Risk Behavior Web-based Survey (KYRBS). Participants and Methods: A total of 54,243 adolescents answered items on purpose and screen time of smartphone use, stress perception, sleep dissatisfaction, depressive symptoms, and suicide-related indicators. The purpose of smartphone usage was categorized as social interaction and non-social interaction, and the daily smartphone screen time was classified into three categories: < 2 hours, ≥ 2 hours but < 4 hours, ≥ 4 hours per day. Results: We determined that, on weekdays, stress perception, sleep satisfaction, depressive symptoms, and suicide-related indicators worsened in both groups that used smartphones for ≥ 4 hours per day. When using smartphone for ≥ 2 hours but < 4 hours per day on weekdays, depending on the smartphone use purpose, mental health outcomes differed between the two groups. On weekends, using smartphones for ≥ 2 hours but < 4 hours per day worsened sleep satisfaction but decreased suicide-related indicators in both groups. Mental health outcomes improved when participants used smartphones for ≥ 2 hours but < 4 hours per day but worsened when smartphones were used for ≥ 4 hours per day on weekends. Conclusion: Recommended screen time of smartphone is different depending on the purpose of smartphone use, and the risk of uncontrolled usage is emphasized, rather than the absolute screen time.

Objective Approximately half of patients with cancer have comorbidities, such as adjustment disorder, major depressive disorder, and delirium. Radiotherapy can cause psychological problems, e.g., the fear of treatment and its side effects, anxiety, depression, and social isolation. Health-related quality of life (QoL) must be determined to evaluate the effectiveness of cancer treatment. We analyzed the clinical, psychological, and sociodemographic factors influencing the QoL of patients with cancer who were undergoing radiotherapy.Methods Twenty-six patients undergoing radiotherapy (10 male, 16 female) were included. Sociodemographic and clinical data were collected prior to radiotherapy. Psychosocial factors were assessed by self-reported questionnaires before, immediately after, and 3 months after radiotherapy. A multivariate regression analysis identified factors affecting QoL at each time point.Results Patients’ diagnoses were breast, cervical, prostate, endometrial, rectal, hypopharyngeal, laryngeal, liver, gallbladder, esophageal, ovarian, lung, and skin cancers. Before radiotherapy, better resilience was significantly associated with a higher QoL score (R2=0.199, p=0.033). Immediately after radiotherapy, financial difficulty was significantly associated with a lower QoL score (R2=0.274, p=0.010). Three months after radiotherapy, the presence of chronic disease (R2=0.398, p=0.002) and the severity of nausea and vomiting were significantly associated with a lower QoL score (R2=0.278, p=0.014).Conclusion Resilience, financial difficulty, the presence of chronic diseases, and the severity of nausea and vomiting significantly influenced the QoL of patients with cancer who were undergoing radiotherapy. Factors affecting QoL varied at each time point. Thus, patients with cancer should undergo regular mental health assessments, including assessments of QoL. Multidimensional (physical, psychological, and social) approaches and individualized time-based interventions are needed to improve the QoL of cancer patients undergoing radiotherapy.

Purpose: This study investigated the prevalence and correlates of problematic internet use (PIU) in a large sample of adolescents based on the type of internet service used. Materials and Methods: The study was conducted from 2008 to 2010, and 223,542 adolescents aged 12 to 18 years participated in the study. The participants responded to a self-report questionnaire including items for demographic factors, internet usage time, most used internet service and mental health. The PIU was assessed with the Internet Addiction Proneness Scale for Youth-Short Form. Results: The overall prevalence rate of PIU was 5.2%, and the prevalence rates stratified by sex were 7.7% in boys and 3.8% in girls. The distribution of most used internet services was significantly different across sexes. The most commonly used internet services were gaming (58.1%) in boys and blogging (22.1%) and messenger/chatting (20.3%) in girls. The odds ratio for PIU was significantly different according to the most used internet service; using the internet mostly for pornography compared to information searching had the highest odds ratio (4.526-fold higher). Depressive episodes, suicidal ideation, and suicidal attempts were significantly associated with higher odds ratios for PIU (1.725-, 1.747- and 1.361-fold, respectively). Conclusion: The present study identified clinically important information about PIU in adolescents. The distribution of PIU has different patterns based on sex and specific internet services. Studies of PIU with well-defined methodology and assessment tools for PIU of each specific internet service are needed.